Cloud computing technologies develop rapidly in recent years. With deployment of a large quantity of network-intensive applications in a cloud-platform data center network, network bandwidth inside a cloud computing system becomes a bottleneck resource in a cloud computing environment. How to reduce bandwidth consumption inside the cloud computing system is an important issue that needs to be considered during system deployment.
In a cloud computing system, a virtual machine is a basic unit for providing a cloud computing service. With running of the system, a large quantity of data is exchanged between different virtual machines, and occupies system network bandwidth resources. Therefore, scheduling and management of virtual machines directly determine bandwidth consumption inside the entire system, and further affect performance and a response speed of the entire system. To resolve the foregoing problem, a Traffic-aware Virtual Machine Placement Problem (TVMPP) optimization algorithm is proposed in other approaches. In this method, deployment locations of the virtual machines in the system are dynamically adjusted for a purpose of a minimum overall communication cost among the virtual machines such that intra-system data traffic generated among the virtual machines is controlled in a physical machine, or controlled in a same physical adjacency domain with a relatively small communication cost as far as possible. Consequently, intra-system network resources are used more efficiently, and the entire performance and response speed of the system are improved.
According to the TVMPP optimization algorithm, each virtual machine in the cloud computing system is considered as a network node. An adjacency degree between any two nodes is defined as a product of an inter-node communication cost and inter-node traffic. A deployment location of a virtual machine is adjusted according to an adjacency degree status of network nodes in the system. The adjustment is based on a minimum cut algorithm, and a basic idea is as follows. A higher communication cost and larger traffic between two nodes indicate a more urgent requirement for reducing the communication cost between the two nodes. However, it is discovered after a large quantity of tests that, the TVMPP optimization algorithm with an even appropriate general idea usually cannot lead to an expected adjustment effect in an actual application, and makes it difficult to effectively resolve a bandwidth consumption problem in the system.